[…] With machine-learning technology, retailers can address the common—and costly—problem of having too much or too little fresh food in stock.

Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers’ revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1

It’s also more complex than ever. Fresh food is perishable, demand is highly variable, and lead times are often uncertain. Furthermore, many retailers now carry broader fresh-food assortments that include exotic and hard-to-find items, as well as “ultrafresh” items with a shelf life of no more than one or two days. Retailers are constantly having to make difficult trade-offs when placing orders with fresh-food suppliers: order too much, and the food goes to waste; order too little, and you lose sales and erode customer loyalty. But with demand fluctuating daily, how can retailers know the right amount to order? […]